A reasoning system as used herein is one comprising rules structured to simulate a particular, desired reasoning process. Expert systems are examples of such reasoning systems in which the reasoning methodologies of one or more experts are modeled, so that upon subsequent provision of input data values to the system, the system simulates the expert reasoning to use the input values and reach some conclusion of the modelled reasoning methodology.
The need to adapt automated reasoning systems to address the problems of uncertainty associated with input data values as well as the operation of the rules comprising the system is recognized in the art. There is a recurrent need in such systems to aggregate the uncertain information. Such information must be aggregated to determine the degree to which the premise of a given rule has been satisfied, to propagate the amount of uncertainty through the firing of a given rule, to summarize the findings provided by various rules or knowledge sources and to detect inconsistencies among different sources of knowledge (including conclusions provided by fired rules). Ad hoc procedures for addressing uncertainty have been formulated, as have procedures based on probability theory. Examples of such procedures are set forth in "Artificial Intelligence" by P. H. Winston, Addison-Wesley Publishing Company, Inc., copyright 1984, 1977, pp. 191-197.
These procedures for addressing uncertainty, however, as well as others known in the art, are deficient because they disregard the fundamental issues to be addressed in the operation of an automated reasoning system. Examples of such fundamental issues include the proper representation of information and meta-information, the allowable inference paradigms suitable for the representation, and the explicitly programmable control of such inferences. Thus, the unknown procedures for addressing uncertainty do not readily lend themselves to integration into known, sophisticated automated reasoning systems. Attempts at such integration have been grossly deficient in a variety of respects. For example, some approaches lack expressiveness in their representation paradigms in that they cannot differentiate ignorance (i.e., a lack of information) from conflict (i.e., inconsistent information). Other approaches require that unrealistic assumptions be imposed on the rule hypotheses, for example assumptions of mutual exclusivity and exhaustiveness, and imposed on the evidence used to evaluate rules, for example conditional independence of individual pieces of evidence, all in order to provide a uniform set of combining guidelines for defining the plausible inferences.
It is therefore a principal object of the present invention to provide a system, and method for operation thereof, for reasoning with uncertainty in an automated reasoning system, that is not subject to the aforementioned problems and disadvantages.